Adaptive learning with AI is supposed to make learning more personal and efficient. How do SMEs’ needs differ from those of larger companies in this regard, hence requiring their own development?

Thomas Kruczinski: SMEs have different requirements than large companies. For example

• monetary resources - SMEs often have more limited financial resources than larger firms; therefore, they may need more cost-effective solutions or more flexible pricing models
• number of employees - Smaller workforces may mean that learning needs are more individual and that a one-size-fits-all solution may not be adequate. Furthermore, there may simply not be enough expertise within the company, which is perfectly normal in view of the ever-broadening training landscape
• business focus - SMEs may have specific business requirements that differ from larger companies, and this fact alone may mean that certain skills need to be acquired more quickly and efficiently
• organizational culture - Due to the difference in the number of employees, the SME’s culture is affected differently in the training context than a large company’s. This means, for example, that greater emphasis is placed on collaboration and knowledge sharing, so learning solutions should be more socially focused.

Developments for SMEs should focus on the specific challenges and opportunities relevant to companies of this size. These include, for example, cost-effective pricing models, simpler implementation, and greater flexibility. There are, of course, many other opportunities for SME employee training in the context of AI-supported adaptive learning. It is important, though, that businesses select the option that best meets their needs and not simply the cheapest, which may not provide the necessary functionality and support.

Should the use of AI training relate primarily to the sequence of learning modules, the structure within modules, or assistance with individual tasks?

Thomas Kruczinski: The learning platform we use works with AI to offer individualized learning suggestions and training for SME employees. The AI analyzes a large number of data points to create a comprehensive picture of each employee’s learning needs, including an initial classification through an upstream assessment in terms of specific skills and competencies. There are also factors such as previous learning success, as well as individual learning preferences and styles.

Based on this data, the AI suggests the most appropriate courses for the employee, taking into account the courses’ sequence and structure. By constantly analyzing the employees’ learning progress and success, the AI can continuously adapt and improve its recommendations to ensure an optimal learning experience.
Due to the opportunities presented by AI in the field of Natural Language Processing (NLP), which is currently undergoing rapid development - especially through ChatGPT - the use of AI-supported chatbots will also play an important role in serving as tutorial support and assistance for learning content. For example, exercises can be automatically generated "on-the-flow" to the individual’s knowledge level, or AI-supported content can be presented to the learner that aims to reduce barriers, e.g., in simple language, as a summary, or translated into the native language. In this realm in particular, the potential for adaptive-AI systems is very high and will lead to completely new approaches in the development of learning content.
In summary, the use of AI can be considered in regard to the recommendation of courses, the sequence of learning content, as well as to complementing and assisting existing learning content.

Adapted learning systems adjust to learners, but is their success gauged according to the extent to which they match users’ preferences or, ultimately - as in the past - is the outcome level they achieve paramount?

Thomas Kruczinski: Adaptive learning systems are measured against both the outcomes they produce and the degree to which they comply with individual learner’s preferences. An upstream assessment and a transcript of the employees’ qualifications can result in better customization to their needs. Constant tracking during the training also allows adjustments to be made at any time to ensure that learners are on track and achieving their learning goals.
When preferences and interests are taken into account, it can help improve engagement and motivation, which in turn can lead to better learning outcomes. At the same time, it is important that the adaptive learning system be focused on outcomes to ensure that employees acquires the skills and knowledge necessary to successfully complete their tasks and advance their careers when viewed from an economic perspective. A balanced combination of both aspects can help keep employees motivated while effectively achieving their learning goals.

Does the use of adaptive learning in SMEs have any type of "track record"?

Thomas Kruczinski: Some studies show that adaptive learning systems can help improve participants’ learning performance by creating a personalized environment that adapts to their individual needs. This can help them learn faster and more effectively, which in turn can lead to higher motivation and satisfaction.
Another benefit of using adaptive learning in SMEs is the reduction of training expenditures. Through the use of online learning platforms, SMEs can optimize their training budgets with the result that a larger number of employees can enhance their qualifications without increasing the cost.

There are, however, also some issues. These include, for example, the need to train and support employees in the use of the learning platforms and to make the complexity of the technology as clear and user friendly as possible.
Let it be said that there is still a lot of room for research into the impact of adaptive learning in SMEs, but initial field reports show that adaptive learning methods hold promise as a way to improve the employees’ learning performance while reducing training costs. Hence, adaptive learning appears to be a resource-saving way for SMEs to position themselves competitively now and for the future.